Systems and methods for generating a neural network model for image processing

ABSTRACT

The disclosure relates to a system and a method for generating a neural network model for image processing by interacting with at least one client terminal. The method may include receiving via a network, a plurality of first training samples from the at least one client terminal. The method may also include training a first neural network model based on the plurality of first training samples to generate a second neural network model. The method may further include transmitting, via the network, the second neural network model to the at least one client terminal.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/233,200, filed on Dec. 27, 2018, which is a Continuation ofInternational Application No. PCT/CN2018/109135, filed on Sep. 30, 2018,the contents of each of which are incorporated herein by reference.

TECHNICAL FIELD

The disclosure generally relates to medical image processing, and morespecifically relates to systems and methods for determining a neuralnetwork model for image processing.

BACKGROUND

Medical imaging systems, such as a CT system, a PET system, an MRIsystem, are typically used for clinical examinations and medicaldiagnoses. The medical imaging system(s) may scan an object to generatescanning data, and further reconstruct one or more images based on thescanning data. The reconstructed image(s) should be further processed.The processing of the reconstructed image(s) may include imagedenoising, image enhancement, image super-resolution processing, imageartifact removing, etc. Generally, the processing of a reconstructedimage may be performed by a client terminal (e.g., a computer).Nowadays, with the development of machine learning, a reconstructedimage can be processed using a trained neural network model. The trainedneural network model may be generated and/or updated by the clientterminal via training a neural network model. However, the trainingand/or updating of the neural network model may require strong computingcapacity, which may occupy a large number of computing resource (e.g.,CPUs) on the client terminal if the neural network model is trained onthe client terminal. Thus, it is desirable to provide a method and asystem for training a neural network model that does not occupy oroccupies less computing resources of the client terminal.

SUMMARY

According to an aspect of the present disclosure, a system forgenerating a neural network model for image processing by interactingwith at least one client terminal is provided. The system may include anetwork configured to facilitate communication of at least one serverdevice in the system and the at least one client terminal. The at leastone server device includes at least one processor and at least onestorage device storing a set of instructions, the at least one processorbeing in communication with the at least one storage device. When theexecutable instructions are executed, the executable instructions maycause the system to implement a method. The method may include receivingvia a network, a plurality of first training samples from the at leastone client terminal. The method may also include training a first neuralnetwork model based on the plurality of first training samples togenerate a second neural network model. The method may further includetransmitting, via the network, the second neural network model to the atleast one client terminal.

In some embodiments, each of the plurality of first training samples mayinclude a first initial image and a first processed image with respectto the first initial image, the first processed image being generated bythe at least one client terminal via processing the first initial image.

In some embodiments, the first processed image may be generated by theat least one client terminal via processing the first initial imageusing a third neural network model.

In some embodiments, each of the plurality of first training samples mayinclude a first initial image, and to train a first neural network modelbased on the plurality of first training samples, the at least oneprocessor may be further configured to cause the system to for each ofthe plurality of first training samples, process the first initial imageto obtain a first processed image, and train the first neural networkmodel based on the plurality of first training samples and a pluralityof first processed images corresponding to the plurality of firsttraining samples, respectively.

In some embodiments, the at least one processor is further configured tocause the system to receive via the network, a first test result of thesecond neural network model from the at least one client terminal, anddetermine the second neural network model as a target neural networkmodel for image processing in response to a determination that the firsttest result satisfies a first condition

In some embodiments, the first test result of the second neural networkmodel may include an evaluation score of the second neural networkmodel, and the at least one processor is further configured to cause thesystem to determine whether the evaluation score of the second neuralnetwork model is greater than a threshold and determine that the firsttest result satisfies the first condition in response to a determinationthat the evaluation score of the second neural network model is greaterthan the threshold.

In some embodiments, the evaluation score of the second neural networkmodel may be determined by evaluating one or more first test imagesaccording to one or more quality parameters relating to each of the oneor more test images. The one or more test images may be generated by theat least one client terminal via processing one or more second initialimages using the second neural network model, and the one or morequality parameters may include at least one of a noise level, aresolution, a contrast ratio, or an artifact level.

In some embodiments, the one or more quality parameters relating to theone or more test images may be evaluated by the at least one clientterminal using an analytic hierarchy process (AHP).

In some embodiments, the at least one processor may be furtherconfigured to cause the system to receive, via the network, the one ormore second initial images and the one or more test images from the atleast one client terminal in response to the determination that the testresult satisfies the first condition, and update the plurality of firsttraining samples with the received one or more second initial images andthe one or more test images.

In some embodiments, the at least one processor may be furtherconfigured to cause the system to in response to a determination thatthe first test result does not satisfy the first condition, determinethe first neural network model as the target neural network model forimage processing.

In some embodiments, the at least one processor may be furtherconfigured to cause the system to transmit the target neural networkmodel to the at least one client terminal over the network.

In some embodiments, the at least one processor may be furtherconfigured to cause the system to obtain a second test result for thetarget neural network model from the at least one client terminal,determine whether the target neural network model needs to be updatedbased on the second test result, and train the target neural networkmodel using a plurality of second training samples to obtain a trainedtarget neural network model in response to a determination that thesecond test result of the target neural network model does not satisfy asecond condition.

In some embodiments, the at least one processor is further configured tocause the system to obtain the second test result for the target neuralnetwork model periodically, or obtain the second test result for thetarget neural network model in response to a request to update thetarget neural network model received from the at least one clientterminal.

In some embodiments, the target neural network model for imageprocessing may be used for at least one of image denoising, imageenhancement, image super-resolution processing, image artifact removing,image diagnosis, or image identification.

In some embodiments, the system may further comprise a plurality ofserver devices distributed connected to the network. Each of theplurality of server devices may be configured to provide a correspondingneural network model for image processing including at least one ofimage denoising, image enhancement, image super-resolution processing,image artifact removing, image diagnosis, or image identification.

According to another aspect of the present disclosure, a system forgenerating a neural network model for image processing is provided. Thesystem may include at least one client terminal, at least one server;and a network configured to facilitate communication between the atleast one client terminal and the at least one server device in thesystem. The at least one server device may include at least oneprocessor and at least one storage device storing a set of instructions,the at least one processor being in communication with the at least onestorage device. When the executable instructions are executed, theexecutable instructions may cause the system to implement a method. Themethod may include receiving, by the at least one processor, a pluralityof first training samples from the at least one client terminal. Themethod may also include training, by the at least one processor, a firstneural network model using the plurality of first training samples togenerate a second neural network model and transmitting, by the at leastone processor, the second neural network model to the at least oneclient terminal. The method may further include generating, by the atleast one client terminal, a first test result of the second neuralnetwork model received from the at least one server device anddetermining, by the at least one processor, the second neural networkmodel as a target neural network model for image processing in responseto a determination that the first test result satisfies a firstcondition.

According to another aspect of the present disclosure, a method forgenerating a neural network model for image processing by interactingwith at least one client terminal is provided. The method may beimplemented on a computing device having one or more processors and acomputer-readable storage medium. The method may include receiving via anetwork, a plurality of first training samples from the at least oneclient terminal. The method may also include training a first neuralnetwork model based on the plurality of first training samples togenerate a second neural network model. The method may further includetransmitting, via the network, the second neural network model to the atleast one client terminal.

According to another aspect of the present disclosure, a non-transitorycomputer readable medium may include instructions. When executed by atleast one processor, the executions may cause the at least one processorto implement a method. The method may be implemented on a computingdevice having one or more processors and a computer-readable storagemedium. The method may include receiving via a network, a plurality offirst training samples from the at least one client terminal. The methodmay also include training a first neural network model based on theplurality of first training samples to generate a second neural networkmodel. The method may further include transmitting, via the network, thesecond neural network model to the at least one client terminal.

According to another aspect of the present disclosure, a method forgenerating a neural network model for image processing is provided. Themethod may be implemented on a system including a computing devicehaving one or more processors and a computer-readable storage medium andat least one client terminal. The method may include receiving, by theat least one processor, a plurality of first training samples from theat least one client terminal. The method may also include training, bythe at least one processor, a first neural network model using theplurality of first training samples to generate a second neural networkmodel and transmitting, by the at least one processor, the second neuralnetwork model to the at least one client terminal. The method mayfurther include generating, by the at least one client terminal, a firsttest result of the second neural network model received from the atleast one server device and determining, by the at least one processor,the second neural network model as a target neural network model forimage processing in response to a determination that the first testresult satisfies a first condition.

According to another aspect of the present disclosure, a non-transitorycomputer readable medium may include instructions. When executed by atleast one processor, the executions may cause the at least one processorto implement a method. The method may include receiving, by the at leastone processor, a plurality of first training samples from the at leastone client terminal. The method may also include training, by the atleast one processor, a first neural network model using the plurality offirst training samples to generate a second neural network model andtransmitting, by the at least one processor, the second neural networkmodel to the at least one client terminal. The method may furtherinclude generating, by the at least one client terminal, a first testresult of the second neural network model received from the at least oneserver device and determining, by the at least one processor, the secondneural network model as a target neural network model for imageprocessing in response to a determination that the first test resultsatisfies a first condition.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1A is a schematic diagram illustrating an exemplary medical systemaccording to some embodiments of the present disclosure;

FIG. 1B is a schematic diagram illustrating an exemplary medical systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device according to some embodimentsof the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a mobile device according to some embodiments ofthe present disclosure;

FIG. 4 is a block diagram illustrating an exemplary server deviceaccording to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for determininga neural network model for image processing according to someembodiments of the present disclosure;

FIG. 6 is a block diagram illustrating an exemplary server deviceaccording to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for determiningtraining sample(s) according to some embodiments of the presentdisclosure; and

FIG. 8 is a flowchart illustrating an exemplary process for testing aneural network model according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “unit,” “module,” and/or“block” used herein are one method to distinguish different components,elements, parts, section or assembly of different level in ascendingorder. However, the terms may be displaced by another expression if theyachieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 220 as illustrated in FIG. 2 ) may beprovided on a computer readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedof connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The disclosure relates to systems and methods for determining a targetneural network model for image processing. In the present disclosure,the target neural network model may be generated via a server device,and may be tested via a client terminal. The server device may have astronger computing capacity than the client terminal, which may shortenthe time of training the target neural network model and may not need tooccupy computing resources (e.g., CPUs) of the client terminal. Theclient terminal may store a plurality of reconstructed image(s), whichmay be convenient to test the target neural network model. To determinethe target neural network model, the systems and methods may receive aplurality of training samples from at least one client terminal via anetwork. The systems and methods may train a first neural network modelbased on the plurality of training samples to generate a second neuralnetwork model. The systems and methods may transmit the second neuralnetwork model to the at least one client terminal via the network, andreceive a test result of the second neural network model from the atleast one client terminal via the network. The systems and methods maydetermine the second neural network model as the target neural networkmodel for image processing in response to a determination that the testresult satisfies a condition. The target neural network model for imageprocessing may be used for at least one of image denoising, imageenhancement, image super-resolution process, image artifact removing,image diagnosis, or image identification. In some embodiments, aplurality of server devices may be distributed connected to the network.Each of the plurality of server devices may be configured to provide acorresponding neural network model for image processing including atleast one of image denoising, image enhancement, image super-resolutionprocessing, image artifact removing, image diagnosis, or imageidentification.

FIG. 1A is a schematic diagram illustrating an exemplary medical systemaccording to some embodiments of the present disclosure. In someembodiments, the medical system 100 may be a single-modality system or amulti-modality system. Exemplary single-modality systems may include acomputed tomography (CT) system, a computed tomography angiography (CTA)system, a digital radiography (DR) system, a positron emissiontomography (PET) system, a single photon emission computed tomography(SPECT) system, a magnetic resonance imaging (MRI) system, a radiationtherapy (RT) system, etc. Exemplary multi-modality systems may include aCT-PET system, a MRI-PET system, etc. In some embodiments, the medicalsystem 100 may include modules and/or components for performing imagingand/or related analysis.

Merely by way of example, as illustrated in FIG. 1 , the medical system100 may include a medical device 110, a network 120, one or more clientterminals 130, a server device 140, and a storage device 150. Thecomponents in the medical system 100 may be connected in various ways.Merely by way of example, the medical device 110 may be connected to theclient terminal(s) 130 directly or through the network 120. As anotherexample, the medical device 110 may be connected to the server device140 directly or through the network 120. As a further example, theclient terminal(s) 130 may be connected to another component of themedical system 100 (e.g., the server device 140) via the network 120. Asstill a further example, the storage device 150 may be connected toanother component of the medical system 100 (e.g., the medical device110, the client terminal(s) 130, the server device 140) directly orthrough the network 150.

The medical device 110 may acquire imaging data relating to at least onepart of an object. The imaging data relating to at least one part of anobject may include an image (e.g., an image slice), projection data, ora combination thereof. In some embodiments, the imaging data may be atwo-dimensional (2D) imaging data, a three-dimensional (3D) imagingdata, a four-dimensional (4D) imaging data, or the like, or anycombination thereof. The object may be a biological object (e.g., apatient, an animal) or a non-biological object (e.g., a man-madeobject). In some embodiments, the medical device 110 may include animaging device, an interventional medical device, or the like. Exemplaryimaging devices may include a PET scanner, a CT scanner, a DR scanner, aMRI scanner, or the like, or a combination thereof. Exemplaryinterventional medical devices may include a radiation therapy (RT)device, an ultrasound treatment device, a thermal treatment device, asurgical intervention device, or the like, or a combination thereof.

The network 120 may facilitate exchange of information and/or data forthe medical system 100. In some embodiments, one or more components ofthe medical system 100 (e.g., the medical device 110, the clientterminal(s) 130, the server device 140, or the storage device 150) maycommunicate information and/or data with one or more other components ofthe medical system 100 via the network 120. For example, the clientterminal(s) 130 and/or the server device 140 may obtain scanning datafrom the medical device 110 via the network 120. As another example, theserver device 140 may obtain user instructions from the clientterminal(s) 130 via the network 120.

In some embodiments, the network 120 may have a distributed networkarchitecture. In some embodiments, the medical system 100 may include aplurality of server devices distributed connected to the network 120. Insome embodiments, the network 120 may be any type of wired or wirelessnetwork, or combination thereof. The network 120 may be and/or include apublic network (e.g., the Internet), a private network (e.g., a localarea network (LAN), a wide area network (WAN)), etc.), a wired network(e.g., an Ethernet network), a wireless network (e.g., an 802.11network, a Wi-Fi network, etc.), a cellular network (e.g., a Long TermEvolution (LTE) network), a frame relay network, a virtual privatenetwork (“VPN”), a satellite network, a telephone network, routers,hubs, switches, server computers, and/or any combination thereof. Merelyby way of example, the network 120 may include a cable network, awireline network, a fiber-optic network, a telecommunications network,an intranet, a wireless local area network (WLAN), a metropolitan areanetwork (MAN), a public telephone switched network (PSTN), a Bluetooth™network, a ZigBee™ network, a near field communication (NFC) network, orthe like, or any combination thereof. In some embodiments, the network120 may include one or more network access points. For example, thenetwork 120 may include wired and/or wireless network access points suchas base stations and/or internet exchange points through which one ormore components of the medical system 100 may be connected to thenetwork 120 to exchange data and/or information.

The client terminal(s) 130 may include a mobile device 130-1, a tabletcomputer 130-2, a laptop computer 130-3, or the like, or any combinationthereof. In some embodiments, the mobile device 130-1 may include asmart home device, a wearable device, a mobile device, a virtual realitydevice, an augmented reality device, or the like, or any combinationthereof. In some embodiments, the smart home device may include a smartlighting device, a control device of an intelligent electricalapparatus, a smart monitoring device, a smart television, a smart videocamera, an interphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a bracelet, a footgear,eyeglasses, a helmet, a watch, clothing, a backpack, a smart accessory,or the like, or any combination thereof. In some embodiments, the mobiledevice may include a mobile phone, a personal digital assistant (PDA), agaming device, a navigation device, a point of sale (POS) device, alaptop, a tablet computer, a desktop, or the like, or any combinationthereof. In some embodiments, the virtual reality device and/or theaugmented reality device may include a virtual reality helmet, virtualreality glasses, a virtual reality patch, an augmented reality helmet,augmented reality glasses, an augmented reality patch, or the like, orany combination thereof. For example, the virtual reality device and/orthe augmented reality device may include a Google Glass™, an OculusRift™, a Hololens™, a Gear VR™, etc.

In some embodiments, the client terminal(s) 130 may receive informationand/or instructions inputted by a user, and send the receivedinformation and/or instructions to the medical device 110 or the serverdevice 140. For example, the client terminal(s) 130 may send a scaninstruction to the medical device 110 to proceed a scan on the object.As another example, the client terminal(s) 130 may send a reconstructioninstruction to the server device 140 to proceed reconstruction of imagesrelated to the object. Alternatively or additionally, the clientterminal(s) 130 may receive data and/or information from the medicaldevice 110 or the server device 140. For example, the client terminal(s)130 may receive scanning data generated by the medical device 110. Asanother example, the client terminal(s) 130 may receive a reconstructedimage from the server device 140. In some embodiments, the clientterminal(s) 130 may be part of the server device 140.

In some embodiments, the client terminal(s) 130 may be a computer andperform partial functions of the server device 140. For example, theclient terminal(s) 130 may process data related to the object (e.g.,basic information of the object, medical information, etc.). Theprocessing of the data related to the object may include addition,deletion, ranking, screening, analyzing, or the like, or any combinationthereof. As another example, the client terminal(s) 130 may processimage data (e.g., an image) and/or scanning data (e.g., projectiondata). The processing of the image data and/or the scanning data mayinclude image reconstruction, image segmentation, image amplification,image reduction, image denoising, image enhancement, imagesuper-resolution processing, image artifact removing, image diagnosis,image identification, or the like, or any combination thereof. Merely byway of example, the client terminal(s) 130 may reconstruct an image ofthe object based on the scanning data. In some embodiments, the clientterminal(s) 130 may generate a denoised image by processing an initialimage using, for example, a neural network model for image denoising.The client terminal(s) 130 may evaluate one or more quality parametersrelating to the denoised image using an analytic hierarchy process(AHP), and further determine an evaluation score of the neural networkmodel for image denoising. In some embodiments, the client terminal(s)130 may transmit the evaluation score of the neural network model forimage denoising to the server device 140. The server device 140 maydetermine whether the neural network model for image denoising needs tobe updated or replaced. The server device 140 may update the neuralnetwork model for image denoising using a plurality of training samples.

In some embodiments, the server device 140 may be a computer, a userconsole, a single server or a server group, etc. The server group may becentralized or distributed. In some embodiments, the server device 140may be local or remote. For example, the server device 140 may accessinformation and/or data stored in the medical device 110, the clientterminal(s) 130, and/or the storage device 150 via the network 120. Asanother example, the server device 140 may be directly connected to themedical device 110, the client terminal(s) 130 and/or the storage device150 to access stored information and/or data. In some embodiments, theserver device 140 may be implemented on a cloud platform. Merely by wayof example, the cloud platform may include a private cloud, a publiccloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or any combination thereof.

The server device 140 may process data and/or information obtained fromthe medical device 110, the client terminal(s) 130, and/or the storagedevice 150. For example, the server device 140 may obtain scanning data(e.g., projection data) from the medical device 110, and reconstruct animage of the object based on the scanning data. As another example, theserver device 140 may obtain a plurality of training samples from clientterminal(s) 130 and generate a trained neural network model for imageprocessing, including image denoising, image enhancement, imagesuper-resolution processing, image artifact removing, image diagnosis,or image identification. In some embodiments, the server device 140 maybe a server group, including a plurality of server devices distributedconnected to the network 120. Each of the plurality of server devicesmay be configured to provide a corresponding neural network model forimage processing, including image denoising, image enhancement, imagesuper-resolution processing, image artifact removing, image diagnosis,or image identification. In some embodiments, the server device 140 maybe implemented on a computing device 200 having one or more componentsas illustrated in FIG. 2 . Detailed descriptions of the server device140 may be found elsewhere in the present disclosure (e.g., FIG. 1B, andthe descriptions thereof).

The storage device 150 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 150 may store dataor information obtained from the medical device 110, the clientterminal(s) 130 and/or the server device 140. For example, the storagedevice 150 may store scanning data generated by the medical device 110.As another example, the storage device 150 may store processed image(s)received from the client terminal(s) 130 and/or the server device 140.As a further example, the storage device 150 may store trained neuralnetwork model(s) generated by the server device 140. In someembodiments, the storage device 150 may store data and/or instructionsthat the client terminal(s) 130 and/or server device 140 may execute oruse to perform exemplary methods/systems described in the presentdisclosure. In some embodiments, the storage device 150 may include amass storage device, removable storage device, a volatile read-and-writememory, read-only memory (ROM), or the like, or any combination thereof.Exemplary mass storage may include a magnetic disk, an optical disk, asolid-state drive, etc. Exemplary removable storage may include a flashdrive, a floppy disk, an optical disk, a memory card, a zip disk, amagnetic tape, etc. Exemplary volatile read-and-write memory may includea random access memory (RAM). Exemplary RAM may include a dynamic RAM(DRAM), a double date rate synchronous dynamic RAM (DDR SDRAM), a staticRAM (SRAM), a thyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM),etc. Exemplary ROM may include a mask ROM (MROM), a programmable ROM(PROM), an erasable programmable ROM (EPROM), an electrically erasableprogrammable ROM (EEPROM), a compact disk ROM (CD-ROM), and a digitalversatile disk ROM, etc. In some embodiments, the storage device 130 maybe implemented on a cloud platform. Merely by way of example, the cloudplatform may include a private cloud, a public cloud, a hybrid cloud, acommunity cloud, a distributed cloud, an inter-cloud, a multi-cloud, orthe like, or any combination thereof.

In some embodiments, the storage device 150 may be connected to thenetwork 120 to communicate with one or more other components in themedical system 100 (e.g., the medical device 110, the server device 140,the client terminal(s) 130, etc.). One or more components in the medicalsystem 100 may access the data or instructions stored in the storagedevice 150 via the network 120. In some embodiments, the storage device150 may be directly connected to or communicate with one or more othercomponents in the medical system 100 (e.g., the medical device 110, theserver device 140, the client terminal(s) 130, etc.). In someembodiments, the storage device 150 may be part of the server device140.

It should be noted that the above description of the medical system 100is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. For example, the assemblyand/or function of the medical system 100 may be varied or changedaccording to specific implementation scenarios. Merely by way ofexample, some other components may be added into the medical system 100,such as a power supply module that may supply power to one or morecomponents of the medical system 100, and other devices or modules.

FIG. 1B is a schematic diagram illustrating an exemplary medical systemaccording to some embodiments of the present disclosure. As illustratedin FIG. 1B, the medical system 200 may include a plurality of serverdevices 140 such as a server device 142-1, a server device 142-2, . . ., a server device 142-N, etc. In some embodiments, at least one of theplurality of sever devices may provide computing services for imageprocessing (e.g., image reconstruction, image segmentation, etc.) asdescribed elsewhere in the present disclosure. In some embodiments, oneserver device may be configured to train and/or generate a single onetype of neural network model for image processing. For example, theserver device 142-1 may be configured to train and generate a neuralnetwork model for image denoising, the server device 142-2 may beconfigured to train and/or generate a neural network for imageenhancement, etc. Alternatively, one server device may be configured totrain and/or generate more than one type of neural network model forimage processing. For example, the server device 142-N may be configuredto train and/or generate a neural network model for image denoising, aneural network model for image enhancement, a neural network model forimage artifact removing, a neural network model for imagesuper-resolution processing, etc.

In some embodiments, a server device (e.g., the server device 142-1) maytrain a preliminary neural network model (e.g., the first neural networkmodel as described in FIG. 5 ) using a plurality of training samples todetermine a trained neural network model (e.g., the second neuralnetwork model as described in FIG. 5 ). The server device (e.g., theserver device 142-1) may determine the trained neural network model as atarget neural network model for image processing in response to adetermination that a test result of the trained neural network modelsatisfies a condition. In some embodiments, the server device (e.g., theserver device 142-1) may update the target neural network model byupdating the plurality of training samples if the target neural networkmodel needs to be updated.

In some embodiments, a server device (e.g., the server device 142-1) maybe any suitable computer(s), such as a laptop, a tablet computer, adesktop, etc. Each of the plurality of server devices 140 may include atleast one processor. The at least one processor may include amicrocontroller, a microprocessor, a reduced instruction set computer(RISC), an application specific integrated circuits (ASICs), anapplication-specific instruction-set processor (ASIP), a centralprocessing unit (CPU), a graphics processing unit (GPU), a physicsprocessing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field programmable gate array (FPGA), an advancedRISC machine (ARM), a programmable logic device (PLD), any circuit orprocessor capable of executing one or more functions, or the like, orany combinations thereof.

The plurality of server devices 140 may be distributed connected to thenetwork 146. In some embodiments, the plurality of server devices 140may be connected to and/or communicate with each other via the network146 (e.g., a wireless connection, a wired connection, or a combinationthereof). Alternatively, at least one of the plurality of server devices140 may be connected to and/or communicate with other server device(s)directly. For example, the server device 142-1 may be connected to theserver device 142-2 directly. The client terminal(s) 130 may beconnected to or communicate with one of the plurality of serverdevice(s) 140 via the network 120 (or the network 146). The clientterminal(s) 130 may also be connected to the storage device 144 via thenetwork 120 as indicated by the dotted arrow in FIG. 1B. In someembodiments, the storage device 144 may be connected to at least one ofthe plurality of server devices 140 directly as indicated by the solidarrow in FIG. 1B. Alternatively, the storage device 144 may be connectedto at least one of the plurality of server devices 140 via the network146.

In some embodiments, the network 146 may include any suitable network(e.g., a wide area network (WAN), a local area network (LAN), wiredand/or wireless network access points, etc.) that can facilitate theexchange of information and/or data for the server device(s) 140. Merelyby way of example, the network 146 may include a cable network, awireline network, a fiber-optic network, a telecommunications network,an intranet, a wireless local area network (WLAN), a metropolitan areanetwork (MAN), a public telephone switched network (PSTN), a Bluetooth™network, a ZigBee™ network, a near field communication (NFC) network, orthe like, or any combination thereof. It should be noted that, in someembodiments, the network 146 may be omitted.

In some embodiments, the storage device 144 may store data,instructions, and/or any other information. For example, the storagedevice 144 may store data obtained from the client terminal(s) 130and/or at least one of the plurality of server devices 140. As anotherexample, the storage device 144 may store algorithms and/or instructionsthat at least one of the plurality of server devices 140 may execute oruse to perform exemplary medical applications described in the presentdisclosure. As a further example, the storage device 144 may storetrained neural network model(s) generated by at least one of theplurality of server devices 140.

It should be noted that the above description of the server device 140is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, at least two components of theplurality of server devices 140 may be integrated into a console. Asanother example, some other components/modules may be added into thecomputing cluster, such as a network switch, a workflow network server,etc. In some embodiments, the storage device 144 may be integrated intoone or more of the plurality of server devices 140. In some embodiments,the network 146 may be omitted.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device according to some embodimentsof the present disclosure. In some embodiments, the server device 140and/or the client terminal(s) 130 may be implemented on the computingdevice 200 and configured to perform functions thereof disclosure in thepresent disclosure.

The computing device 200 may be a general purpose computer or a specialpurpose computer. Both may be used to implement the medical system 100of the present disclosure. For example, the server device 140 of themedical system 100 may be implemented on the computing device 200, viaits hardware, software program, firmware, or a combination thereof. InFIG. 2 , only one such computing device is shown purely for convenience.One of ordinary skill in the art would understood at the time of filingof this application that the computer functions relating to the medicalsystem 100 as described herein may be implemented in a distributedmanner on a number of similar devices/platforms, to distribute thecomputing load.

The computing device 200, for example, may include COM ports 250connected to and from a network (e.g., the network 120) connectedthereto to facilitate data communications. In some embodiments, the COMports 250 may transmit to and receive information or data from any oneof the modules of the server device 140 and/or the client terminal(s)130. In some embodiments, the COM ports 250 may include a wired port(e.g., a Universal Serial Bus (USB) port, a High Definition MultimediaInterface (HDMI) port, etc.) or a wireless port (a Bluetooth port, aninfrared interface, a WiFi port, etc.).

The computing device 200 may also include a processor 220 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU), etc.),in the form of one or more processors, for executing computinginstructions. Computing instructions may include routines, programs,objects, components, data structures, procedures, modules, andfunctions, which perform particular functions described herein. In someembodiments, the processor 220 may include a microcontroller, amicroprocessor, a reduced instruction set computer (RISC), anapplication specific integrated circuits (ASICs), anapplication-specific instruction-set processor (ASIP), a centralprocessing unit (CPU), a graphics processing unit (GPU), a physicsprocessing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field programmable gate array (FPGA), an advancedRISC machine (ARM), a programmable logic device (PLD), any circuit orprocessor capable of executing one or more functions, or the like, orany combinations thereof. For example, the processor 220 may include amicrocontroller to process data (e.g., scanning data) from the medicaldevice 110 (e.g., a CT scanner) for image reconstruction.

The computing device 200 may also include an internal communication bus210, program storage and data storage of different forms, for example, adisk 270, and a read only memory (ROM) 230, or a random access memory(RAM) 240, for various data files to be processed and/or transmitted bythe computer. The disk 270 may include, for example, a floppy disk, anoptical disk, a zip disk, etc. The ROM 230 may include a mask ROM(MROM), a programmable ROM (PROM), an erasable programmable ROM (PEROM),an electrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. The RAM 240 may includea dynamic RAM (DRAM), a double date rate synchronous dynamic RAM (DDRSDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. In some embodiments, the computingdevice 200 may also include program instructions stored in the ROM 230,the RAM 240, and/or another type of non-transitory storage medium to beexecuted by the processor 220. The methods and/or processes of thepresent disclosure may be implemented as the program instructions. Thecomputing device 200 may also include an I/O 260, supportinginput/output between the computer and other components therein. Thecomputing device 200 may also receive programs and data via networkcommunications.

Merely for illustration, only one processing unit and/or processor isdescribed in the computing device 200. However, it should be noted thatthe computing device 200 in the present disclosure may also includemultiple processing units and/or processors, thus operations and/ormethod steps that are performed by one processing unit and/or processoras described in the present disclosure may also be jointly or separatelyperformed by the multiple processing units and/or processors. Forexample, the processing unit and/or processor of the computing device200 executes both operation A and operation B. As in another example,operation A and operation B may also be performed by two differentprocessing units and/or processors jointly or separately in thecomputing device 200 (e.g., the first processor executes operation A,and the second processor executes operation B; or the first and secondprocessors jointly execute operations A and B).

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a mobile device according to some embodiments ofthe present disclosure. In some embodiments, the server device 140and/or the client terminal(s) 130 may be implemented on the mobiledevice 300 and configured to perform functions thereof disclosure in thepresent disclosure. As illustrated in FIG. 3 , the mobile device 300 mayinclude a communication platform 310, a display 320, a graphicsprocessing unit (GPU) 330, a central processing unit (CPU) 340, an I/O350, a memory 360, and a storage 390. In some embodiments, any othersuitable component, including but not limited to a system bus or acontroller (not shown), may also be included in the mobile device 300.In some embodiments, a mobile operating system 370 (e.g., iOS™,Android™, Windows Phone™) and one or more applications 380 may be loadedinto the memory 360 from the storage 390 in order to be executed by theCPU 340 and/or the GPU 330. The applications 380 may include a browseror any other suitable mobile apps for receiving and renderinginformation relating to image processing or other information in theserver device 140 and/or the client terminal(s) 130. User interactionswith the information stream may be achieved via the I/O 350 and providedto other components of the medical system 100 via the network 120.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or any other type of work station or terminaldevice. A computer may also act as a server if appropriately programmed.

FIG. 4 is a block diagram illustrating an exemplary server deviceaccording to some embodiments of the present disclosure. In someembodiments, the server device 140 may be implemented on the computingdevice 200 and/or the mobile device 300. As illustrated in FIG. 4 , theserver device 140 may include a receiving module 402, a training module404, a transmitting module 406, a determination module 408, an updatingmodule 410, and a first storing module 412.

The receiving module 402 may be configured to receive information and/ordata (e.g., scanning data, image data) from the at least one clientterminal (e.g., the client terminal(s) 130). In some embodiments, thereceiving module 402 may receive a plurality of first training samplesfrom at least one client terminal via a network (e.g., the network 120).Each of the plurality of first training samples may include a firstinitial image and a first processed image with respect to the firstinitial image. The first processed image may exhibit a higher qualitythan the first initial image. In some embodiments, the receiving module402 may receive the plurality of first training samples from two or moreclient terminals at different time periods or at the same time period.For example, the receiving module 402 may receive a portion of theplurality of first training samples from a first client terminal at afirst time period (or a current time period), and receive anotherportion of the plurality of first training samples from a second clientterminal at a second time period (or a prior time period). As anotherexample, the receiving module 402 may receive a portion of the pluralityof first training samples from a first client terminal and anotherportion of the plurality of first training samples from a second clientterminal at the same time period.

In some embodiments, the receiving module 402 may receive a test resultrelating to a neural network model from the at least one client terminalvia the network (e.g., the network 120). Merely by way of example, thereceiving module 402 may receive a first test result relating to asecond neural network model from the at least one client terminal. Thesecond neural network model may be generated by training a first neuralnetwork model using the plurality of first training samples. In someembodiments, the first test result of the second neural network modelmay include a first evaluation score of the second neural network model.The first evaluation score of the second neural network model may bedetermined by evaluating one or more first test images according to oneor more quality parameters relating to the one or more first testimages. As another example, the receiving module 402 may receive asecond test result for a target neural network model from the at leastone client terminal. The target neural network model may be determinedbased on the first neural network model and/or the second neural networkmodel. In some embodiments, the second test result of the target neuralnetwork model may include a second evaluation score of the target neuralnetwork model. The second evaluation score of the target neural networkmodel may be determined by evaluating one or more second test imagesaccording to one or more quality parameters relating to the one or moresecond test images. Details regarding the determination of the (first orsecond) evaluation score of a neural network model (e.g., the secondneural network model, the target neural network model) may be foundelsewhere in the present disclosure (e.g., FIG. 8 and the descriptionsthereof).

The training module 404 may be configured to train a preliminary neuralnetwork model (e.g., a first neural network model) based on theplurality of first training samples to generate a trained neural networkmodel (e.g., a second neural network model). In some embodiments, thefunction of the second neural network model may depend on the pluralityof first training samples. For example, if the plurality of firsttraining samples include first initial images and first denoised images,the second neural network model may be used for image denoising. Asanother example, if the plurality of first training samples includefirst initial images and first enhanced images, the second neuralnetwork model may be used for image enhancement. More descriptionsregarding the generation of the second neural network model may be foundelsewhere in the present disclosure (e.g., operation 503 of the process500 and the descriptions thereof).

The transmitting module 406 may be configured to transmit informationand/or data to the at least one client terminal (e.g., the clientterminal(s) 130) via the network (e.g., the network 120). In someembodiments, the transmitting module 406 may transmit the second neuralnetwork model to the at least one client terminal. In some embodiments,the transmitting module 406 may transmit the second neural network modelto the at least one client terminal periodically, e.g., once a week,once a month, etc., or when the second neural network model isgenerated, or in response to a request to install or update the secondneural network model from the at least one client terminal.

The determination module 408 may be configured to determine whether thefirst test result satisfies a first condition. In some embodiments, thedetermination module 408 may determine whether the first evaluationscore of the second neural network model is greater than a firstthreshold. If the first evaluation score of the second neural networkmodel is greater than the first threshold, the determination module 408may determine that the first test result satisfies the first condition.In response to a determination that the first test result satisfies thefirst condition, the determination module 408 may determine the secondneural network model as a target neural network model for imageprocessing. Alternatively, in response to a determination that the firsttest result does not satisfy the first condition, the determinationmodule 408 may determine the first neural network model as a targetneural network model for image processing.

The updating module 410 may be configured to determine whether thetarget neural network model needs to be updated. In some embodiments,the updating module 410 may determine whether the second test resultsatisfies a second condition. If the updating module 410 determines thatthe second test result satisfies the second condition, the updatingmodule 410 may determine that the target neural network model does notneed to be updated. If the updating module 410 determines that thesecond test result does not satisfy the second condition, the updatingmodule 410 may determine that the target neural network model needs tobe updated. More descriptions regarding the updating of the targetneural network model may be found elsewhere in the present disclosure(e.g., operation 517 of the process 500 and the descriptions thereof).

The first storing module 412 may be configured to store informationand/or data generated during the process 500. For example, the firststoring module 412 may store the first training samples received fromthe at least one client terminal. As another example, the first storingmodule 412 may store one or more neural network models (e.g., the firstneural network model, the second neural network model, the target neuralnetwork model).

In some embodiments, the modules in the server device 140 may beconnected to or communication with each other via a wired connection ora wireless connection. The wired connection may include a metal cable,an optical cable, a hybrid cable, or the like, or any combinationthereof. The wireless connection may include a Local Area Network (LAN),a Wide Area Network (WAN), a Bluetooth, a ZigBee, a Near FieldCommunication (NFC), or the like, or a combination thereof).

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. In someembodiments, two or more of the modules may be combined into a singlemodule. For example, the receiving module 402 and the transmittingmodule 406 may be integrated into one single module configured toperform the functions thereof.

FIG. 5 is a flowchart illustrating an exemplary process for determininga neural network model for image processing according to someembodiments of the present disclosure. In some embodiments, one or moreoperations of the process 500 may be implemented in the medical system100 illustrated in FIG. 1 . For example, the process 500 may be storedin the storage device 150 in the form of instructions (e.g., anapplication), and invoked and/or executed by the server device 140(e.g., the server devices 142-1, 142-2, . . . , 142-N as illustrated inFIG. 1B, the processor 220 of the computing device 200 as illustrated inFIG. 2 , the CPU 340 of the mobile device 300 as illustrated in FIG. 3 ,one or more modules of the server device 140 as illustrated in FIG. 4 ,or the like). The operations of the illustrated process below areintended to be illustrative. In some embodiments, the process 500 may beaccomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of the process 500 as illustrated inFIG. 5 and described below is not intended to be limiting.

In 501, the server device 140 (e.g., the receiving module 402) mayreceive a plurality of first training samples from at least one clientterminal (e.g., the client terminal(s) 130) via a network (e.g., thenetwork 120).

In some embodiments, each of the plurality of first training samples mayinclude a first initial image and a first processed image with respectto the first initial image. The first processed image may exhibit ahigher quality than the first initial image. As used herein, the qualityof an image may be denoted by the noise level of the image, the artifactof the image, the contrast ratio of the image, the resolution of theimage, or the like, or a combination thereof. In some embodiments, thefirst initial image and the first processed image may be generated basedon the same scanning data (or projection data). For example, the firstinitial image and the first processed image may be reconstructed usingtwo different reconstruction techniques, which may result in thedifference of image quality. Exemplary reconstruction techniques may befound elsewhere in the present disclosure, and the descriptions thereofare not repeated herein. As another example, the first processed imagemay be generated by the at least one client terminal via processing thefirst initial image. For example, the first processed image may begenerated by the at least one client terminal via processing the firstinitial image using a neural network model stored in the at least oneclient terminal. Details regarding the generation of the first trainingsamples by the at least one client terminal may be found elsewhere inthe present disclosure (e.g., FIG. 7 and the relevant descriptionsthereof). Alternatively, each of the plurality of first training samplesreceived from the at least one client terminal may include a firstinitial image. For each of the plurality of first training samples, theserver device 140 may process a first initial image to obtain a firstprocessed image. In some embodiments, the first processed image and thecorresponding first initial image may constitute a first trainingsample. That is, the plurality of first training samples may be updatedto include a plurality of first initial images received from the atleast one client terminal and a plurality of first processed imagesgenerated by the server device 140.

In some embodiments, the processing of a first initial image may includeimage denoising, image enhancement, image super-resolution processing,image artifact removing, or the like, or any combination thereof. Merelyby way of example, the server device 140 may generate the firstprocessed image (also referred to as a first denoised image) byperforming an image denoising operation on the first initial image usingan image denoising algorithm. The first processed image may exhibit alower noise level than the first initial image. Exemplary imagedenoising algorithms may include a Gaussian filtering, an anisotropicfiltering (AF), a neighboring filtering, a total variation minimization,a kernel regression algorithm, a wavelet-based algorithm, anon-local-means (NL-means) algorithm, or the like. As another example,the server device 140 may generate the first processed image (alsoreferred to as a first enhanced image) by performing an imageenhancement operation on the first initial image using an imageenhancement algorithm. The first enhanced image may have better contrastratio with respect to the first initial image. In some embodiments,exemplary image enhancement algorithms may include a histogramequalization algorithm, a wavelet transform algorithm, a partialdifferential equation algorithm, a Retinex algorithm, or the like. As afurther example, the server device 140 may generate the first processedimage by processing the first initial image using an imagesuper-resolution processing algorithm. The first processed image mayexhibit a higher resolution than the first initial image. Exemplaryimage super-resolution processing algorithms may include a sparserepresentation algorithm, a self-exemplars algorithm, a Naïve Bayesalgorithm, a deep learning algorithm, or the like. As a further example,the server device 140 may generate the first processed and/or optimizedimage by decreasing artifacts in the first initial image. The firstprocessed image may exhibit less artifacts than the first initial image.In some embodiments, the artifacts may include aliasing artifacts, beamhardening artifacts, scattering artifacts, artifacts caused by partialvolume effect, metal artifacts, ring artifacts, stair-step artifacts,cone beam artifacts, windmill artifacts, truncation artifacts, motionartifacts, or the like, or any combination thereof. Different kinds ofartifacts may be removed according to its corresponding correctionalgorithms. For example, the scattering artifacts may be removedaccording to a scattering correction algorithm, for example, aconvolution algorithm, a model assessing algorithm, a deconvolutionalgorithm, a Monte Carlo simulation algorithm, a dual energy-windowtechnique, or the like.

In some embodiments, the receiving module 402 may receive the pluralityof first training samples from two or more client terminals at differenttime periods. For example, the receiving module 402 may receive aportion of the plurality of first training samples from a first clientterminal at a first time period (or a current time period), and receiveanother portion of the plurality of first training samples from a secondclient terminal at a second time period (or a prior time period).Alternatively or additionally, the receiving module 402 may receive theplurality of first training samples from two or more client terminals atthe same time period. For example, the receiving module 402 may receivea portion of the plurality of first training samples from a first clientterminal and another portion of the plurality of first training samplesfrom a second client terminal at the same time period.

In 503, the server device 140 (e.g., the training module 404) may traina first neural network model based on the plurality of first trainingsamples to generate a second neural network model. In some embodiments,the function of the second neural network model may depend on theplurality of first training samples. For example, if the plurality offirst training samples include first initial images and first denoisedimages, the second neural network model may be used for image denoising.As another example, if the plurality of first training samples includefirst initial images and first enhanced images, the second neuralnetwork model may be used for image enhancement.

In some embodiments, the first neural network model may be a preliminaryneural network model. Alternatively or additionally, the preliminaryneural network model may be a pre-trained neural network model for imageprocessing, e.g., image denoising, image enhancement, imagesuper-resolution processing, or image artifact removing, etc. In someembodiments, the pre-trained neural network model may be obtained bytraining a neural network model using training samples acquired by amedical device (e.g., the medical device 110) in a prior time periodbefore a current time period. Thus, the pre-trained neural network modelmay need to be updated to be suitable for image processing at present.

Exemplary neural network models may include a convolutional neuralnetwork model (e.g., a multi-scale convolutional neural network model, asuper-resolution convolutional neural network model, a denoisingconvolutional neural network model), a perceptron neural network model,a deep trust network model, a stack self-coding network model, arecurrent neural network model (e.g., a long short term memory (LSTM)neural network model, a hierarchical recurrent neural network model, abi-direction recurrent neural network model, a second-order recurrentneural network model, a fully recurrent network model, an echo statenetwork model, a multiple timescales recurrent neural network (MTRNN)model), or the like, or any combination thereof.

In some embodiments, the first neural network model may include one ormore preliminary parameters. The preliminary parameter(s) may beadjusted and/or updated during the training of the first neural networkmodel using a neural network training algorithm. Exemplary neuralnetwork training algorithms may include a back propagation algorithm, agradient descent algorithm, a Newton's algorithm, a conjugate gradientalgorithm, a Quasi-Newton algorithm, a Levenberg Marquardt algorithm, orthe like, or any combination thereof.

In some embodiments, a first initial image may be inputted into thefirst neural network model to generate an actual output (also referredto as a first predict image). The first processed image with respect tothe first initial image may be considered as a desired output. Thetraining module 404 may compare the actual output (e.g., the firstpredict image) with the desired output (e.g., the first processed image)using a loss function. The loss function may measure a differencebetween the actual output and the desired output. During the training ofthe first neural network model, a plurality of iterations may beperformed to adjust and/or update the preliminary parameter(s) of thefirst neural network model until a termination condition is satisfied.Exemplary termination conditions may include that an updated lossfunction with the updated parameter(s) obtained in an iteration is lessthan a predetermined threshold, that a certain iteration count ofiterations are performed, that the loss function converges such that thedifferences of the values of the updated loss function obtained inconsecutive iterations are within a threshold, etc. After the terminatedcondition is satisfied, the second neural network model may bedetermined based on the updated parameter(s).

In 505, the server device 140 (e.g., the transmitting module 406) maytransmit the second neural network model to the at least one clientterminal (e.g., the client terminal(s) 130) via the network (e.g., thenetwork 120). In some embodiments, the transmitting module 406 maytransmit the second neural network model to the at least one clientterminal when the second neural network model is generated.Alternatively, the transmitting module 406 may transmit the secondneural network model to the at least one client terminal periodically,e.g., once a week, once a month, etc. Alternatively, the training module406 may transmit the second neural network model to the at least oneclient terminal in response to a request to install or update the secondneural network model from the at least one client terminal.

In some embodiments, the transmitting module 406 may transmit the secondneural network model to one or more client terminals via the network.For example, the transmitting module 406 may transmit the second neuralnetwork model to the at least one client terminal (e.g., the mobiledevice 130-1) via the network from which the plurality of first trainingsamples are received and/or another client terminal (e.g., the laptopcomputer 130-3) that does not provide training samples to the serverdevice 140.

In 507, the server device 140 (e.g., the receiving module 402) mayreceive a first test result of the second neural network model from theat least one client terminal (e.g., the client terminal(s)) via thenetwork (e.g., the network 120). In some embodiments, the first testresult of the second neural network model may include a first evaluationscore of the second neural network model. The first evaluation score ofthe second neural network model may be determined by evaluating one ormore first test images according to one or more quality parametersrelating to the one or more first test images. The one or more firsttest images may be generated by the at least one client terminal viaprocessing one or more second initial images using the second neuralnetwork model. The one or more quality parameters may include a noiselevel, a resolution, an artifact level, a contrast ratio, or the like.Details regarding the determination of the (first) evaluation score of aneural network model (e.g., the second neural network model) may befound elsewhere in the present disclosure (e.g., FIG. 8 and thedescriptions thereof).

In 509, the server device 140 (e.g., the determination module 408) maydetermine whether the first test result satisfies a first condition. Insome embodiments, the determination module 408 may determine whether thefirst evaluation score of the second neural network model is greaterthan a first threshold. The first threshold may be a default value ofthe medical system 100, or be set or adjusted by a user. If the firstevaluation score of the second neural network model is greater than thefirst threshold, the determination module 408 may determine that thefirst test result satisfies the first condition. In some embodiments, inresponse to the determination that the test result satisfies the firstcondition, the server device 140 may receive the second initial imagesand the one or more test images via the network and update the pluralityof first training samples with the received one or more second initialimages and the one or more test images.

In response to a determination that the first test result satisfies thefirst condition, the process 500 may proceed to 511. In 511, the serverdevice 140 (e.g., the determination module 408) may determine the secondneural network model as a target neural network model for imageprocessing. Alternatively, in response to a determination that the firsttest result does not satisfy the first condition, the process 500 mayproceed to 513. In 513, the server device 140 (e.g., the determinationmodule 408) may determine the first neural network model as a targetneural network model for image processing. The server device 140 (e.g.,the transmitting module 406) may transmit the target neural networkmodel (i.e., the first neural network model or the second neural networkmodel) to the at least one client terminal over the network.

In some embodiments, when the target neural network model implemented inthe client terminal has been used for a period, the target neuralnetwork model may not be suitable for processing (initial) images at thepresent time due to changes of the system and/or external conditions.Thus, the target neural network model may need to be updated.

In 515, the server device 140 (e.g., the receiving module 402) mayreceive and/or obtain a second test result for the target neural networkmodel from the at least one client terminal. In some embodiments, thesecond test result of the target neural network model may include asecond evaluation score of the target neural network model. The secondevaluation score of the target neural network model may be determined byevaluating one or more second test images according to one or morequality parameters relating to the one or more second test images. Theone or more second test images may be generated by the at least oneclient terminal via processing one or more third initial images usingthe target neural network model. The one or more quality parameters mayinclude a noise level, a resolution, an artifact level, a contrastratio, or the like. Details regarding the determination of the (second)evaluation score of a neural network model (e.g., the target neuralnetwork model) may be found elsewhere in the present disclosure (e.g.,FIG. 8 and the descriptions thereof).

In some embodiments, the receiving module 402 may obtain the second testresult for the target neural network model periodically, for example,once a week, once a month, once a half year, or the like. Alternatively,the receiving module 402 may obtain the second test result for thetarget neural network model in response to a request to update thetarget neural network model received from the at least one clientterminal. In some embodiments, the request may be entered into the atleast one client terminal by a user, or may be default settings of theclient terminal(s).

In 517, the server device 140 (e.g., the updating module 410) maydetermine whether the target neural network model needs to be updated.In some embodiments, the updating module 410 may determine whether thesecond test result satisfies a second condition. If the updating module410 determines that the second test result satisfies the secondcondition, the updating module 410 may determine that the target neuralnetwork model does not need to be updated. If the updating module 410determines that the second test result does not satisfy the secondcondition, the updating module 410 may determine that the target neuralnetwork model needs to be updated. In some embodiments, the secondcondition may be the same as or different from the first condition.Specifically, the updating module 410 may determine whether the secondevaluation score of the target neural network model is greater than asecond threshold. The second threshold may be a default value of themedical system 100, or be set or adjusted by a user. In someembodiments, the second threshold may be the same as or different fromthe first threshold. If the second evaluation score of the target neuralnetwork model is greater than the second threshold, the updating module410 may determine that the second test result satisfies the secondcondition, that is, the target neural network model does not need to beupdated. In response to a determination that the target neural networkmodel does not need to be updated, the process 500 may proceed to 519.In 519, the server device 140 may update the first neural network modelusing the target neural network model. The server device 140 may furtherstore the target neural network model in the first storing module 412.

Alternatively, if the second evaluation score of the target neuralnetwork model is less than or equal to the second threshold, theupdating module 410 may determine that the second test result does notsatisfy the second condition, that is, the target neural network modelneeds to be updated. In response to a determination that the targetneural network model needs to be updated, the process 500 may update theplurality of first training samples and proceed to perform operations501 through 513. In 501, the server device 140 (e.g., the receivingmodule 402) may obtain and/or receive the plurality of first trainingsamples. The plurality of first training samples may be updated using aplurality of second training samples. For example, the plurality ofsecond training samples may be added in the plurality of first trainingsamples. As another example, at least one portion of the plurality offirst training samples may be replaced by the plurality of secondtraining samples. In some embodiments, the plurality of second trainingsamples may include at least one portion of the plurality of firsttraining samples. Alternatively, each of the plurality of secondtraining samples may be different from one of the plurality of firsttraining samples. The obtaining of the second training samples may be ina manner similar to that of the first training samples, and thedescriptions thereof are not repeated. In 503, the server device 140(e.g., the training module 404) may train the first neural network model(i.e., the target neural network model) using the plurality of firsttraining samples (e.g., the second training samples) to obtain thesecond neural network model. The first neural network model may beupdated using the target neural network model as described in operation519. The second neural network model may be also referred to as atrained target neural network model. The training of the target neuralnetwork model may be in a manner similar to that of the first neuralnetwork model, and the descriptions thereof are not repeated. Theprocess 500 may repeat operations 505 through 513, and the descriptionsthereof are not repeated.

In the present disclosure, since a server device (e.g., the serverdevice 140) has a stronger computing capacity than the client terminal,the training of a neural network model (e.g., the first neural networkmodel, the target neural network model, etc.) is performed via theserver device (e.g., the server device 140), which may shorten the timeof training the neural network model and may not occupy computingresources of the client terminal(s) 130. Further, the client terminalmay store a plurality of images, which can be processed by the neuralnetwork model to generate a plurality of processed images (i.e., testimages). The testing of the neural network model (e.g., the targetneural network model, etc.) may be performed by the client terminal(e.g., the client terminal(s) 130) via evaluating one or more testimages (processed images by the target neural network model), which maybe convenient to test the neural network model and not occupy largecomputing resources of the client terminal(s) 130.

It should be noted that the above description of the process 500 ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. In some embodiments, thetarget neural network model for image processing may further be used forimage diagnosis, image identification, or the like. In some embodiments,operations 515 through 519 may be omitted.

FIG. 6 is a block diagram illustrating an exemplary server deviceaccording to some embodiments of the present disclosure. In someembodiments, the server device 600 may be implemented on the clientterminal 130 as illustrated in FIG. 1 . As illustrated in FIG. 6 , theserver device 600 may include an obtaining module 602, a processingmodule 604, a determination module 606, an evaluation module 608, and asecond storing module 610.

The obtaining module 602 may be configured to obtain information and/ordata (e.g., scanning data, image data). In some embodiments, theobtaining module 602 may obtain a plurality of first initial images. Insome embodiments, the first initial image may exhibit a first imagequality. As used herein, the first image quality may be defined by oneor more quality parameters of the first initial image, such as a noiselevel, a contrast ratio, a resolution, an artifact level, etc. In someembodiments, the obtaining module 602 may obtain one or more secondinitial images. In some embodiments, a second initial image may besimilar to or the same as one of the plurality of first initial images.Alternatively, a second initial image may be different from any one ofthe plurality of first initial images. In some embodiments, theobtaining module 602 may obtain the (first and/or second) initial imagesfrom one or more components of the medical system 100, for example, thesecond storing module 610 of the client terminal 130, the medical device110, the server device 140, a storage device (e.g., the storage device150), etc. Alternatively or additionally, the obtaining module 602 mayobtain the (first or second) initial images from an external source viathe network 120, e.g., a medical database, picture archiving andcommunication system (PACS), etc.

In some embodiments, the obtaining module 602 may obtain a neuralnetwork model. The neural network model may include the first neuralnetwork model, the second neural network model, the target neuralnetwork model as described in FIG. 5 . In some embodiments, theobtaining module 602 may obtain the neural network model from the secondstoring module 610 of the client terminal 130, the storage 390 of themobile device 300, etc. In some embodiments, the obtaining module 602may obtain the neural network model from the server device (e.g., thefirst storing module 410, the training module 404, the updating module410, etc.). Alternatively, the obtaining module 602 may obtain theneural network model in response to a request to evaluate the neuralnetwork model.

The processing module 604 may be configured to generate a plurality offirst processed images. Each of the plurality of first processed imagesmay correspond to one of the plurality of first initial images. In someembodiments, a first processed image may exhibit a second image quality.The second image quality of the first processed image may be greaterthan the first image quality of a corresponding first initial image. Insome embodiments, the processing module 604 may process a first initialimages to obtain a corresponding first processed image. The processingof the first initial image may include image denoising, imageenhancement, image super-resolution processing, image artifact removing,or the like, or any combination thereof. Alternatively or additionally,the processing module 604 may process the first initial image using athird neural network model for image processing, such as imagedenoising, image enhancement, image super-resolution processing, imageartifact removing, etc.

In some embodiments, the processing module 604 may generate one or moretest images by processing the one or more second initial images usingthe neural network model obtained by the obtaining module 602. In someembodiments, the processing module 604 may input a second initial imageinto the neural network model to generate a corresponding test image(also referred to second processed image). In some embodiments, a testimage processed using the neural network model may have better qualityrelative to the corresponding second initial image.

The determination module 606 may be configured to designate theplurality of first initial images and the plurality of first processedimages as a plurality of training samples. Each of the plurality oftraining samples may include a first initial image and a correspondingprocessed image.

The evaluation module 608 may be configured to evaluate one or morequality parameters relating to each of the one or more test imagesgenerated by the processing module 604. In some embodiments, the one ormore quality parameters may include a noise level, a resolution, anartifact level, a contrast ratio, or the like, or any combinationthereof. The quality of a test image may be assessed by evaluating theone or more quality parameters. In some embodiments, the quality of atest image may be denoted by a score determined by evaluating the one ormore quality parameters of the test image. The higher the score of thetest image is, the higher the quality of the test image may be.

The evaluation module 608 may also be configured to determine anevaluation score of the neural network model based on the evaluations ofthe one or more quality parameters relating to each of the one or moretest images. In some embodiments, the evaluation module 608 maydetermine a score for each test image based on the one or more qualityparameters of the test image. The evaluation module 608 may determinethe evaluation score of the neural network model based on the scores ofthe one or more test images. More descriptions regarding the evaluationof the neural network model may be found elsewhere in the presentdisclosure (e.g., operations 807 and 809 of the process 800 and thedescriptions thereof).

The second storing module 610 may be configured to store informationand/or data generated or used during the processes 700 and 800. Forexample, the second storing module 610 may store the first trainingsample(s), the second initial image(s), and/or the test image(s). Asanother example, the second storing module 610 may store the neuralnetwork model to be evaluated (e.g., the first neural network model, thesecond neural network model, the target neural network model). As afurther example, the second storing module 610 may store the qualityparameter(s), the score of the neural network model, or the like.

In some embodiments, the modules in the server device 600 may beconnected to or communication with each other via a wired connection ora wireless connection. The wired connection may include a metal cable,an optical cable, a hybrid cable, or the like, or any combinationthereof. The wireless connection may include a Local Area Network (LAN),a Wide Area Network (WAN), a Bluetooth, a ZigBee, a Near FieldCommunication (NFC), or the like, or a combination thereof).

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. In someembodiments, any one of the modules may be divided into two or moreunits. For example, the obtaining module 602 may be divided into twounits. A first unit may be configured to obtain training samples, andthe second unit may be configured to obtain a neural network model fromthe server device.

FIG. 7 is a flowchart illustrating an exemplary process for determiningtraining sample(s) according to some embodiments of the presentdisclosure. In some embodiments, one or more operations of the process700 may be implemented in the medical system 100 illustrated in FIG. 1 .For example, the process 700 may be stored in the storage device 150 inthe form of instructions (e.g., an application), and invoked and/orexecuted by the client terminal 130 (e.g., the processor 220 of thecomputing device 200 as illustrated in FIG. 2 , the CPU 340 of themobile device 300 as illustrated in FIG. 3 , one or more modules of theserver device 600 as illustrated in FIG. 6 , or the like). Theoperations of the illustrated process below are intended to beillustrative. In some embodiments, the process 700 may be accomplishedwith one or more additional operations not described, and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of the process 700 as illustrated in FIG. 7 and describedbelow is not intended to be limiting. In some embodiments, the pluralityof first training samples and/or the plurality of second trainingsamples described in connection with FIG. 5 may be generated accordingto process 700.

In 702, the server device 600 (e.g., the obtaining module 602) mayobtain a plurality of first initial images. In some embodiments, a firstinitial image may include a two-dimensional (2D) image, athree-dimensional (3D) image, a four-dimensional (4D) image, or thelike, or any combination thereof. A first initial image may be a CTimage, an MRI image, a PET image, or the like, or a combination thereof.

In some embodiments, a first initial image may be reconstructed based onscanning data (e.g., projection data) acquired by a medical device(e.g., the medical device 110) using a first reconstruction technique.In some embodiments, the first reconstruction technique may include aniterative reconstruction technique, an analytical reconstructiontechnique, or the like, or a combination thereof. Exemplary iterativereconstruction techniques may include an algebraic reconstructiontechnique (ART), a simultaneous iterative reconstruction technique(SIRT), a simultaneous algebraic reconstruction technique (SART), anadaptive statistical iterative reconstruction (ASIR) technique, a modelbased iterative reconstruction (MAIR) technique, a sinogram affirmediterative reconstruction (SAFIR) technique, or the like, or acombination thereof. Exemplary analytical reconstruction techniques mayinclude applying an FDK algorithm, a Katsevich algorithm, or the like,or a combination thereof. In some embodiments, the first initial imagemay exhibit a first image quality. As used herein, the first imagequality may be defined by one or more quality parameters of the firstinitial image, such as a noise level, a contrast ratio, a resolution, anartifact level, etc.

In some embodiments, the obtaining module 602 may obtain the pluralityof first initial images from one or more components of the medicalsystem 100, for example, the second storing module 610 of the clientterminal 130, the medical device 110, the server device 140, a storagedevice (e.g., the storage device 150), etc. Alternatively oradditionally, the obtaining module 602 may obtain the plurality of firstinitial images from an external source via the network 120, e.g., amedical database, picture archiving and communication system (PACS),etc.

In 704, the server device 600 (e.g., the processing module 604) maygenerate a plurality of first processed images. Each of the plurality offirst processed images may correspond to one of the plurality of firstinitial images. In some embodiments, a first processed image may exhibita second image quality. The second image quality of the first processedimage may be greater than the first image quality of a correspondingfirst initial image. For example, a first processed image may begenerated by performing an image denoising operation on a correspondingfirst initial image. The first processed image may include less noisesthan the corresponding first initial image. As another example, a firstinitial image may be generated by performing an image enhancement on acorresponding first processed image. The contrast ratio of the firstprocessed image may be higher than that of the corresponding firstinitial image.

In some embodiments, the first processed image may be reconstructedbased on the scanning data (e.g., projection data) associated with thecorresponding first initial image using a second reconstructiontechnique. In some embodiments, the first reconstruction technique maybe different from the second reconstruction technique. The second imagequality of the first processed image and the first image quality of thecorresponding first initial image reconstructed based on the samescanning data but different reconstruction techniques may be different.For example, the noise level of the first initial image reconstructedusing an analytical reconstruction technique may be higher than that ofthe first processed image reconstructed using an iterativereconstruction technique. In some embodiments, the first reconstructiontechnique may be same as the second reconstruction technique. The secondimage quality of the first processed image and the first image qualityof the corresponding first initial image reconstructed based on the sameimage data and the same reconstruction technique but differentreconstruction parameters may be different. For example, the noise levelof a first initial image reconstructed using a smaller slice thickness,a smaller reconstruction matrix, a larger FOV, etc., may be higher thanthat of a corresponding first processed image reconstructed based on asame reconstruction technique using a larger slice thickness, a largerreconstruction matrix, a smaller FOV, etc.

In some embodiments, the processing module 604 may process a firstinitial image to obtain a corresponding first processed image. Theprocessing of the first initial image may include image denoising, imageenhancement, image super-resolution processing, image artifact removing,or the like, or any combination thereof. Details regarding theprocessing of the first initial image(s) may be found elsewhere in thepresent disclosure (e.g., operation 501 of the process 500 and thedescriptions thereof). Alternatively or additionally, the processingmodule 604 may process the first initial image using a third neuralnetwork model for image processing, such as image denoising, imageenhancement, image super-resolution processing, image artifact removing,etc. In some embodiments, the third neural network model may beconfigured to convert a first initial image to a corresponding firstprocessed image. In some embodiments, the third neural network model maybe a configured neural network model stored in the client terminal(s)when the client terminal(s) is installed. The obtaining module 602 mayobtain the neural network model from the second storing module 610 ofthe client terminal 130, the storage 390 of the mobile device 300, etc.In some embodiments, the obtaining module 602 may obtain the neuralnetwork model (e.g., the target neural network model as described inFIG. 5 ) from the server device (e.g., the first storing module 412, thetraining module 404, the updating module 410, etc.).

In 706, the server device 600 (e.g., the determination module 606) maydesignate the plurality of first initial images and the plurality offirst processed images as a plurality of training samples. Each of theplurality of training samples may include a first initial image and acorresponding processed image. In some embodiments, the training samplesmay be used as the first training samples to train the first neuralnetwork model as described in connection with FIG. 5 . Alternatively,the training samples may be used as the second training samples to trainthe target neural network model when the target neural network modelneeds to be updated as described in connection with FIG. 5 .

It should be noted that the above description of the process ofallocating computing resources for medical applications in response torequests for performing the medical applications is merely provided forthe purposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. In someembodiments, the first initial image(s) may have better quality than thefirst processed image(s). For example, a first initial image may be animage generated with high dosages, which includes few noises. Theprocessing module 604 may process the first initial image to generate afirst processed image, which may includes more noises than the firstinitial images. Then the first initial image and the first processedimage may constitute a training sample.

FIG. 8 is a flowchart illustrating an exemplary process for testing aneural network model according to some embodiments of the presentdisclosure. In some embodiments, one or more operations of the process800 may be implemented in the medical system 100 illustrated in FIG. 1 .For example, the process 800 may be stored in the storage device 150 inthe form of instructions (e.g., an application), and invoked and/orexecuted by the client terminal 130 (e.g., the processor 220 of thecomputing device 200 as illustrated in FIG. 2 , the CPU 340 of themobile device 300 as illustrated in FIG. 3 , one or more modules of theserver device 600 as illustrated in FIG. 6 , or the like). Theoperations of the illustrated process below are intended to beillustrative. In some embodiments, the process 800 may be accomplishedwith one or more additional operations not described, and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of the process 800 as illustrated in FIG. 8 and describedbelow is not intended to be limiting. In some embodiments, the firsttest result of the second neural network model as described in 507and/or the second test result of the target neural network model asdescribed in 515 illustrated in FIG. 5 may be determined according toprocess 800.

In 801, the server device 600 (e.g., the obtaining module 602) mayobtain one or more second initial images. Operation 801 may be performedin a manner similar to operation 701 of the process 700, and thedescriptions thereof are not repeated here. In some embodiments, asecond initial image may be similar to or the same as one of theplurality of first initial images as described in FIG. 7 . For example,a second initial image may be a two-dimensional (2D) image, athree-dimensional (3D) image, a four-dimensional (4D) image, or thelike, or any combination thereof. As another example, a second initialimage may be a CT image, an MR image, a PET image, etc. As still anexample, a second initial image may be one of the plurality of firstinitial images as descried in FIG. 7 . In some embodiments, a secondinitial image may be different from any one of the plurality of firstinitial images as described in FIG. 7 . For example, the second initialimage may be reconstructed based on projection data acquired by themedical device 110 via scanning a first subject at a current timeperiod. The plurality of first initial images may be reconstructed basedon projection data acquired by the medical device 110 via scanning asecond subject at a prior time period. The first subject and the secondsubject may be the same or different.

In 803, the server device 600 (e.g., the obtaining module 602) mayobtain a neural network model. In some embodiments, the neural networkmodel may be a trained neural network model for image processing, e.g.,image denoising, image enhancement, image super-resolution processing,image artifact removing, etc. For example, the neural network model maybe the first neural network model, the second neural network model, orthe target neural network model as described in FIG. 5 . In someembodiments, the obtaining module 602 may obtain the neural networkmodel from the second storing module 610 of the client terminal 130, thestorage 390 of the mobile device 300, etc. In some embodiments, theobtaining module 602 may obtain the neural network model from the serverdevice (e.g., the first storing module 410, the training module 404, theupdating module 410, etc.). Alternatively, the obtaining module 602 mayobtain the neural network model in response to a request to evaluate theneural network model.

In 805, the server device 600 (e.g., the processing module 604) maygenerate one or more test images by processing the one or more secondinitial images using the obtained neural network model. In someembodiments, the processing module 604 may input a second initial imageinto the neural network model to generate a corresponding test image(also referred to second processed image). In some embodiments, a testimage processed using the neural network model may have better qualityrelative to the corresponding second initial image. For example, if theneural network model is configured to denoise an image, the test imageprocessed using the neural network model may exhibit a lower noise levelthan the second initial image. As another example, if the neural networkmodel is configured to enhance an image, the test image processed usingthe neural network model may exhibit a higher contrast ratio than thesecond initial image. As still an example, if the neural network modelis configured to decrease artifacts presented in an image, the testimage processed using the neural network model may exhibit lessartifacts than the second initial image. As still an example, if theneural network model is configured to improve resolution of an image,the test image processed using the neural network model may exhibit ahigher resolution than the second initial image.

In 807, the server device 600 (e.g., the evaluation module 608) mayevaluate one or more quality parameters relating to each of the one ormore test images. In some embodiments, the one or more qualityparameters may include a noise level, a resolution, an artifact level, acontrast ratio, or the like, or any combination thereof. The quality ofa test image may be assessed by evaluating the one or more qualityparameters. For example, the lower the noise level of a test image is,the higher the quality of the test image may be. As another example, thelower the noise level of a test image is, and the less artifactspresented in the test image is, the higher the quality of the test imagemay be. As still an example, the higher the contrast ratio of a testimage is, the higher the resolution of the test image is, the lower thenoise level of the test image is, and/or the less artifacts presented inthe test image is, the higher the quality of the test image may be. Insome embodiments, the quality of a test image may be denoted by a scoredetermined by evaluating the one or more quality parameters of the testimage. The higher the score of the test image is, the higher the qualityof the test image may be. In some embodiments, the evaluation module 608may evaluate the one or more quality parameters relating to each of theone or more test images using an image quality assessment technique,such as an analytic hierarchy process (AHP) algorithm, a mean squarederror (MSE) algorithm, a peak signal to noise rate (PSNR) algorithm, astructural similarity (SSIM) algorithm, etc.

Merely be way of example, using the AHP algorithm to evaluate a testimage, each of the one or more quality parameters may be given a scorelevel (e.g., ranging from 1 to 5). For example, the score level of thenoise level of the test image may be 4, the score level of theresolution of the test image may be 4, and the score level of theartifact level of the test image may be 5. The score level of thequality parameter(s) may be determined by a user or according to aquality evaluation model (e.g., a convolution neural network model). Theevaluation module 608 may further determine a weight for each qualityparameter relating to the test image according to a default setting ofthe medical system 100 or an instruction of the user. The evaluationmodule 608 may determine a score of the test image based on the scorelevels and the corresponding weights of the one or more qualityparameters.

In 809, the server device 600 (e.g., the evaluation module 608) maydetermine an evaluation score of the neural network model based on theevaluations of the one or more quality parameters relating to each ofthe one or more test images. In some embodiments, the evaluation module608 may determine a score for each test image based on the one or morequality parameters of the test image. The evaluation module 608 maydetermine the evaluation score of the neural network model based on thescores of the one or more test images. For example, the evaluationmodule 608 may determine a reference value based on the scores of theone or more test images as the evaluation score of the neural networkmodel. In some embodiments, the reference value may include an averagevalue of the scores of the one or more test images, a median of thescores of the one or more test images, a variance of the scores of theone or more test images, a standard deviation of the scores of the oneor more test images, a minimum of the scores of the one or more testimages, a maximum of the scores of the one or more test images, or othervalues from the scores of the one or more test images.

In 811, the server device 600 may transmit a test result of the neuralnetwork model including the evaluation score of the neural network modelto a server device (e.g., the server device 140). In some embodiments,the test result of the neural network model may be denoted by theevaluation score of the neural network model. In some embodiments, theneural network model may be ranked based on the evaluation score of theneural network model into, for example, “A”, “B”, “C”, etc. For example,if the evaluation score of the neural network model is in a first range,the neural network model may be ranked into “A.” If the evaluation scoreof the neural network model is in a second range, the neural networkmodel may be ranked into “B.” An evaluation score in the first range maybe greater than an evaluation score in the second range. The test resultof the neural network model may be denoted by the rank of the neuralnetwork model.

It should be noted that the above description of the process ofallocating computing resources for medical applications in response torequests for performing the medical applications is merely provided forthe purposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example,operations 801 and 803 may be performed simultaneously. As anotherexample, operation 803 may be performed before operation 801. As stillan example, operation 811 may be omitted.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A non-transitory computer readable signal medium may include apropagated data signal with computer readable program code embodiedtherein, for example, in baseband or as part of a carrier wave. Such apropagated signal may take any of a variety of forms, includingelectro-magnetic, optical, or the like, or any suitable combinationthereof. A computer readable signal medium may be any computer readablemedium that is not a computer readable storage medium and that maycommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.Program code embodied on a computer readable signal medium may betransmitted using any appropriate medium, including wireless, wireline,optical fiber cable, RF, or the like, or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C #, VB.NET, Python or the like, conventional procedural programming languages,such as the “C” programming language, Visual Basic, Fortran 2003, Perl,COBOL 2002, PHP, ABAP, dynamic programming languages such as Python,Ruby and Groovy, or other programming languages. The program code mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider) or in a cloud computing environment oroffered as a service such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed object matter requires more features than areexpressly recited in each claim. Rather, inventive embodiments lie inless than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities, properties, andso forth, used to describe and claim certain embodiments of theapplication are to be understood as being modified in some instances bythe term “about,” “approximate,” or “substantially.” For example,“about,” “approximate,” or “substantially” may indicate ±20% variationof the value it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

1-20. (canceled)
 21. A system for generating a neural network model forimage processing by interacting with at least one client terminal,comprising: a network configured to facilitate communication of at leastone server device in the system and the at least one client terminal,wherein the at least one server device each of which includes at leastone processor and at least one storage device storing a set ofinstructions, the at least one processor being in communication with theat least one storage device, wherein when executing the set ofinstructions, the at least one processor is configured to cause thesystem to: receive, via the network, a plurality of first trainingsamples from at least one first client terminal among the at least oneclient terminal, wherein each of the plurality of first training samplesincludes a first initial image and a first processed image with respectto the first initial image, the first processed image being generated bythe at least one client terminal via processing the first initial imageusing a third neural network model; and train a first neural networkmodel based on the plurality of first training samples to generate asecond neural network model.
 22. The system of claim 21, wherein the atleast one processor is further configured to cause the system to:transmit, via the network, the second neural network model to at leastone second client terminal among the at least one client terminal. 23.The system of claim 22, wherein the at least one first client terminaland the at least one second client terminal are the same or differentclient terminals.
 24. The system of claim 22, wherein the at least oneprocessor is further configured to cause the system to: receive via thenetwork, a first test result of the second neural network model from theat least one second client terminal; and determine the second neuralnetwork model as a target neural network model for image processing inresponse to a determination that the first test result satisfies a firstcondition.
 25. The system of claim 24, wherein the first test result ofthe second neural network model includes an evaluation score of thesecond neural network model, and the at least one processor is furtherconfigured to cause the system to: determine whether the evaluationscore of the second neural network model is greater than a threshold;and determine that the first test result satisfies the first conditionin response to a determination that the evaluation score of the secondneural network model is greater than the threshold.
 26. The system ofclaim 25, wherein the evaluation score of the second neural networkmodel is determined by evaluating one or more first test imagesaccording to one or more quality parameters relating to each of the oneor more test images, wherein the one or more test images are generatedby the at least one second client terminal via processing one or moresecond initial images using the second neural network model, and the oneor more quality parameters include at least one of a noise level, aresolution, a contrast ratio, or an artifact level.
 27. The system ofclaim 26, wherein the at least one processor is further configured tocause the system to: receive, via the network, the one or more secondinitial images and the one or more test images from the at least onesecond client terminal in response to the determination that the testresult satisfies the first condition; and update the plurality of firsttraining samples with the received one or more second initial images andthe one or more test images.
 28. The system of claim 24, wherein the atleast one processor is further configured to cause the system to: inresponse to a determination that the first test result does not satisfythe first condition, determine the first neural network model as thetarget neural network model for image processing.
 29. The system ofclaim 28, wherein the at least one processor is further configured tocause the system to: transmit the target neural network model to the atleast one first client terminal over the network.
 30. The system ofclaim 22, the at least one processor is further configured to cause thesystem to: obtain a second test result for the target neural networkmodel from the at least one second client terminal; determine whetherthe target neural network model needs to be updated based on the secondtest result; and train the target neural network model using a pluralityof second training samples to obtain a trained target neural networkmodel in response to a determination that the second test result of thetarget neural network model does not satisfy a second condition.
 31. Thesystem of claim 30, wherein the at least one processor is furtherconfigured to cause the system to: obtain the second test result for thetarget neural network model periodically, or obtain the second testresult for the target neural network model in response to a request toupdate the target neural network model received from the at least onesecond client terminal.
 32. The system of claim 21, wherein the at leastone server device includes at least two server devices, and the at leasttwo server devices are connected to the network through a distributedconnection, and the at least one processor of each server device isconfigured to train the first neural network model based on theplurality of first training samples.
 33. A system for generating aneural network model for image processing, comprising: at least oneclient terminal; at least one server device; and a network configured tofacilitate communication between the at least one client terminal and atleast one server device in the system; wherein the at least one serverdevice includes at least one processor and at least one storage devicestoring a set of instructions, the at least one processor being incommunication with the at least one storage device, wherein whenexecuting the set of instructions, the at least one processor isconfigured to cause the system to: receive, by the at least oneprocessor, a plurality of first training samples from at least one firstclient terminal among the at least one client terminal; train, by the atleast one processor, a first neural network model using the plurality offirst training samples to generate a second neural network model;transmit, by the at least one processor, the second neural network modelto at least one second client terminal of the at least one clientterminal; generate, by the at least one second client terminal, a firsttest result of the second neural network model received from the atleast one server device; and determine, by the at least one processor,the second neural network model as a target neural network model forimage processing in response to a determination that the first testresult satisfies a first condition.
 34. The system of claim 33, whereinthe at least one first client terminal and the at least one secondclient terminal are the same or different client terminals.
 35. Thesystem of claim 33, wherein the at least one server device includes atleast two server devices, and the at least two server devices areconnected to the network through a distributed connection, and the atleast one processor of each server device is configured to train thefirst neural network model based on the plurality of first trainingsamples.
 36. The system of claim 33, wherein the at least one processoris configured to cause the system to: obtain, by the at least one firstclient terminal, a plurality of first initial images from a firstmedical device; generate, by the at least one first client terminal orthe at least one processor, a plurality of first processed images basedon the plurality of first initial images using a third neural networkmodel; and designate, by the at least one first client terminal or theat least one processor, the plurality of first initial images and theplurality of first processed images as the plurality of first trainingsamples.
 37. The system of claim 33, wherein the first test result ofthe second neural network model includes an evaluation score of thesecond neural network model, and the at least one processor is furtherconfigured to cause the system to: determine, by the at least oneprocessor, whether the evaluation score of the second neural networkmodel is greater than a threshold; and determine, by the at least oneprocessor, that the first test result satisfies the first condition inresponse to a determination that the evaluation score of the secondneural network model is greater than the threshold.
 38. The system ofclaim 37, wherein the evaluation score of the second neural networkmodel is determined by evaluating one or more first test imagesaccording to one or more quality parameters relating to each of the oneor more test images, and to generate a first test result relating to thesecond neural network model, the at least one processor is configured tocause the system to: obtain, by the at least one second client terminal,one or more second initial images from a second medical device;generate, by the at least one second client terminal, the one or moretest images by processing the one or more second initial images usingthe second neural network model; and evaluate, by the at least onesecond client terminal, the one or more quality parameters relating toeach of the one or more test images to obtain the evaluation score ofthe second neural network model, wherein the one or more qualityparameters includes at least one of a noise level, a resolution, acontrast ratio, or an artifact level.
 39. The system of claim 38,wherein the at least one processor is further configured to cause thesystem to: receive, by the at least one processor, the one or moresecond initial images and the one or more test images from the at leastone second client terminal in response to the determination that thefirst test result satisfies the first condition; and update, by the atleast one processor, the plurality of first training samples with thereceived one or more second initial images and the one or more testimages.
 40. A method for generating a neural network model for imageprocessing by interacting with at least one client terminal implementedon a computing device having one or more processors and acomputer-readable storage medium, the method comprising: receiving, viaa network, a plurality of first training samples from at least one firstclient terminal among the at least one client terminal, wherein each ofthe plurality of first training samples includes a first initial imageand a first processed image with respect to the first initial image, thefirst processed image being generated by the at least one clientterminal via processing the first initial image using a third neuralnetwork model; and training a first neural network model based on theplurality of first training samples to generate a second neural networkmodel.